{"doi":"10.1101/2020.05.16.20103408","title":"Interpretable Artificial Intelligence for COVID-19 Diagnosis from Chest CT Reveals Specificity of Ground-Glass Opacities","abstract":"Background The use of CT imaging enhanced by artificial intelligence to effectively diagnose COVID-19, instead of or in addition to reverse transcription-polymerase chain reaction (RT-PCR), can improve widespread COVID-19 detection and resource allocation. Methods 904 axial lung window CT slices from 338 patients in 17 countries were collected and labeled. The data included 606 images from COVID-19 positive patients (confirmed via RT-PCR), 224 images of a variety of other pulmonary diseases including viral pneumonias, and 74 images of normal patients. We developed, trained, validated, and tested an object detection model which detects features in three categories: ground-glass opacities (GGOs) for COVID-19, GGOs for non-COVID-19 diseases, and features that are inconsistent with a COVID-19 diagnosis. These collected features are passed into an interpretable decision tree model to make a suggested diagnosis. Results On an independent test of 219 images from COVID-19 positive, a variety of pneumonia, and healthy patients, the model predicted COVID-19 diagnoses with an accuracy of 96.80 % (95% confidence interval [CI], 96.75 to 96.86) , AUC-ROC of 0.9664 (95% CI, 0.9659 to 0.9671) , sensitivity of 98.33% (95% CI, 98.29 to 98.40) , precision of 95.93% (95% CI, 95.83 to 95.99), and specificity of 94.95% (95% CI, 94.84 to 95.05). On an independent test of 34 images from asymptomatic COVID-19 positive patients, our model achieved an accuracy of 97.06% (95% CI, 96.81 to 97.06) and a sensitivity of 96.97% (95% CI, 96.71 to 96.97). Similarly high performance was also obtained for out-of-sample countries, and no significant performance difference was obtained between genders. Conclusion We present an interpretable artificial intelligence CT analysis tool to diagnose COVID-19 in both symptomatic and asymptomatic patients. Further, our model is able to differentiate COVID-19 GGOs from similar pathologies suggesting that GGOs can be disease-specific.","journal":"medRxiv","year":2020,"id":119234,"datarank":1.2664665509944095,"base_score":3.2188758248682006,"endowment":3.2188758248682006,"self_citation_contribution":0.48283137373023016,"citation_network_contribution":0.7836351772641793,"self_endowment_contribution":0.48283137373023016,"citer_contribution":0.7836351772641793,"corpus_percentile":83.73946004486733,"corpus_rank":2103,"citation_count":24,"citer_count":23,"citers_with_citation_signal":19,"citers_with_endowment":19,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5417,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":553775,"name":"Pranav Warman","orcid":"0000-0001-5199-2474","position":1,"is_corresponding":false},{"id":554927,"name":"Ayushman Sharma","orcid":null,"position":2,"is_corresponding":false},{"id":553776,"name":"Puja B. Parikh","orcid":"0000-0002-0048-2789","position":3,"is_corresponding":false},{"id":554928,"name":"Roshan Warman","orcid":null,"position":4,"is_corresponding":false},{"id":554929,"name":"Narayan Viswanadhan","orcid":null,"position":5,"is_corresponding":false},{"id":553777,"name":"Lu Chen","orcid":"0000-0003-1141-8288","position":6,"is_corresponding":false},{"id":553778,"name":"Subhra Mohapatra","orcid":"0000-0001-7636-0174","position":7,"is_corresponding":false},{"id":553779,"name":"Shyam S. Mohapatra","orcid":"0000-0001-5838-0681","position":8,"is_corresponding":false},{"id":89915,"name":"Guillermo Sapiro","orcid":"0000-0001-9190-6964","position":9,"is_corresponding":false},{"id":553774,"name":"Anmol Warman","orcid":"0000-0001-5564-6747","position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":null,"created_at":"2026-07-18T23:14:08.313144Z","pmid":"32511545","pmcid":"PMC7274226","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}